The effects of non-structural components on the dynamic characteristics and vulnerability of concrete structures using ambient vibration tests and Nakamura's criterion

Hadianfard, Mohammad Ali; Jahangiri, Milad; Shojaei, Shahrokh

doi:10.1016/j.soildyn.2022.107492

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Meanwhile, considering the influence of environment, equipment, human and other factors, different noises will inevitably be attached in the actual signal acquisition. Similarly, data C n θ is mixed with Gaussian white noise according to equation (26) to simulate channel interference [47], and the SDI results in table 5 are analyzed,…”

Section: Performance Evaluation Of 3ds-cnn and Msdcmentioning

confidence: 99%

“…That is, 'd 32×32 ' serves as the benchmark for all the data, and the 'height' of the data is multiplied according to the number of channels. In the process of data reconstruction, Gaussian white noise is still attached according to equation (26). The average accuracy ξ ′ of SDI is calculated for 10 times with 3DS-CNN, which are shown in table 12 and figure 22.…”

Section: Influence Of Sensor Placement On Case Studymentioning

confidence: 99%

“…Astroza et al [25] studied the influence of NSC on the evolution of structural parameters of a building structure under construction. Hadianfard et al [26] figured out the effects of NSCs on the dynamic characteristics and the vulnerability indices of concrete structures using microtremor measurements, and found that the natural frequency and damping ratio are both increased when the buildings are under construction. Literature [27][28][29] discussed the influence of NSC on the overall characteristics of the structure under the excitation of seismic waves and pointed out that the NSCs have significant efficacy on accurate vulnerability assessment.…”

Section: Introductionmentioning

confidence: 99%

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A new convolutional neural network-based framework and data construction method for structural damage identification considering sensor placement

Yang

Peng

2023

Meas. Sci. Technol.

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In the application of data driven Structural Damage Identification (SDI) based on supervised deep learning technology, valid data demarcation is the foundation; a convolutional neural network model with learning ability and capability of processing rich signal information is the core. Based on this understanding, this work makes three contributions: Firstly, the structural damage location and severity are jointly demarcated, and the SDI problem is transformed into a multi-classification task. Secondly, a 3D Signal processing Convolutional Neural Networks (3DS-CNN) is designed with an attempt to identify the complex and slight damages using the most basic network structure. Thirdly, a “Major and Subsidiary” Data Construction (MSDC) method integrating the key Intrinsic Mode Function (IMF) is proposed to construct 3D data. Then the proposed schemes are verified by two different structures. The results show that the 3DS-CNN has excellent damage identification ability for small-size data with noise pollution. MSDC method can enrich the feature information of the damage signals and help the network with deep feature excavation, even if the vibration signals are heavily polluted. Going one step further, the impact of sensor placement is discussed, and it is found that when external excitation is obvious, better SDI accuracy can be achieved even using a single sensor signal with slight noise. When the noise interference is obvious, the generalization ability and noise robustness of the network can be enhanced by optimizing sensor placement. In this case, the sensor placement criteria and the sensitive nodes of the structure should be comprehensively and carefully considered to avoid mutual “coupling” interference of data between sensors.

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Section: Performance Evaluation Of 3ds-cnn and Msdcmentioning

confidence: 99%

Section: Influence Of Sensor Placement On Case Studymentioning

confidence: 99%